class_docstr: Main coreference resolution algorithm
docstr: Run the coreference model on a mentions list
docstr_tok: ["Run","the","coreference","model","on","a","mentions","list"]
code: def run_coref_on_mentions(self, mentions):
      """
      Run the coreference model on a mentions list
      """
      best_ant = {}
      best_score = {}
      n_ant = 0
      inp = np.empty((SIZE_SINGLE_IN, len(mentions)))
      for i, mention_idx in enumerate(mentions):
          mention = self.data[mention_idx]
          print("mention.embedding", mention.embedding.shape)
          inp[: len(mention.embedding), i] = mention.embedding
          inp[: len(mention.embedding), i] = mention.features
          inp[: len(mention.embedding), i] = self.data.genre
      score = self.coref_model.get_multiple_single_score(inp.T)
      for mention_idx, s in zip(mentions, score):
          self.mentions_single_scores[mention_idx] = s
          best_score[mention_idx] = s - 50 * (self.greedyness - 0.5)
  
      for mention_idx, ant_list in self.data.get_candidate_pairs(
          mentions, self.max_dist, self.max_dist_match
      ):
          if len(ant_list) == 0:
              continue
          inp_l = []
          for ant_idx in ant_list:
              mention = self.data[mention_idx]
              antecedent = self.data[ant_idx]
              feats_, pwf = self.data.get_pair_mentions_features(
                  antecedent, mention
              )
              inp_l.append(pwf)
          inp = np.stack(inp_l, axis=0)
          score = self.coref_model.get_multiple_pair_score(inp.T)
          for ant_idx, s in zip(ant_list, score):
              self.mentions_pairs_scores[mention_idx][ant_idx] = s
              if s > best_score[mention_idx]:
                  best_score[mention_idx] = s
                  best_ant[mention_idx] = ant_idx
          if mention_idx in best_ant:
              n_ant += 1
              self._merge_coreference_clusters(
                  best_ant[mention_idx], mention_idx
              )
      return (n_ant, best_ant)
  
imports: import os
  import spacy
  import numpy
  from neuralcoref.train.utils import PACKAGE_DIRECTORY, SIZE_SINGLE_IN
  from neuralcoref.train.compat import unicode_
  from neuralcoref.train.document import Document, MENTION_TYPE, NO_COREF_LIST
label_desc: Use image node_id 6 for calling the Coref obj's underlying member method code with example usage: obj.run_coref_on_mentions(mentions) and returns: n_ant,  best_ant